mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2026-04-26 02:14:06 +08:00
Jason
99 lines
3.5 KiB
Python
99 lines
3.5 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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import logging
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from .... import FastDeployModel, ModelFormat
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from .... import c_lib_wrap as C
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class RetinaFace(FastDeployModel):
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def __init__(self,
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model_file,
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params_file="",
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runtime_option=None,
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model_format=ModelFormat.ONNX):
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# 调用基函数进行backend_option的初始化
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# 初始化后的option保存在self._runtime_option
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super(RetinaFace, self).__init__(runtime_option)
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self._model = C.vision.facedet.RetinaFace(
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model_file, params_file, self._runtime_option, model_format)
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# 通过self.initialized判断整个模型的初始化是否成功
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assert self.initialized, "RetinaFace initialize failed."
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def predict(self, input_image, conf_threshold=0.7, nms_iou_threshold=0.3):
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return self._model.predict(input_image, conf_threshold,
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nms_iou_threshold)
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# 一些跟模型有关的属性封装
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# 多数是预处理相关，可通过修改如model.size = [640, 480]改变预处理时resize的大小（前提是模型支持）
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@property
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def size(self):
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return self._model.size
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@property
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def variance(self):
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return self._model.variance
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@property
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def downsample_strides(self):
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return self._model.downsample_strides
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@property
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def min_sizes(self):
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return self._model.min_sizes
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@property
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def landmarks_per_face(self):
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return self._model.landmarks_per_face
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@size.setter
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def size(self, wh):
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assert isinstance(wh, (list, tuple)),\
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"The value to set `size` must be type of tuple or list."
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assert len(wh) == 2,\
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"The value to set `size` must contatins 2 elements means [width, height], but now it contains {} elements.".format(
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len(wh))
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self._model.size = wh
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@variance.setter
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def variance(self, value):
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assert isinstance(v, (list, tuple)),\
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"The value to set `variance` must be type of tuple or list."
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assert len(value) == 2,\
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"The value to set `variance` must contatins 2 elements".format(
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len(value))
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self._model.variance = value
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@downsample_strides.setter
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def downsample_strides(self, value):
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assert isinstance(
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value,
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list), "The value to set `downsample_strides` must be type of list."
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self._model.downsample_strides = value
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@min_sizes.setter
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def min_sizes(self, value):
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assert isinstance(
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value, list), "The value to set `min_sizes` must be type of list."
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self._model.min_sizes = value
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@landmarks_per_face.setter
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def landmarks_per_face(self, value):
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assert isinstance(
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value,
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int), "The value to set `landmarks_per_face` must be type of int."
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self._model.landmarks_per_face = value
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